Radio Access Network Research Articles

Virtualization of a 4G Evolved Packet Core Network Using Network Function Virtualization (NFV) Technology with NS3 for Enterprise and Educational Purpose

The current networks of many operators dispose an increasing variety of purpose built, vertically integrated, and vendor-locked hardware equipment. This makes it difficult to easily scale their radio access and core networks as it requires yet another variety of equipment, leading to increased Time-to-Market and inefficient resource utilization. Furthermore, network equipment are expensive to procure and upgrade (increased CAPEX), and are difficult to adapt and program for new services (increased OPEX). These trends have recently spurred several efforts to redesign various components of mobile networks, notably the 4G Evolved Packet Core (EPC). With regards to this, the present work proposes a solution based on Network Function Virtualization (NFV), which can be deployed for educational or enterprises purpose like in Data Centers, or network nodes with a fine-grained QoS, while maintaining the scalability of the virtualized network function entities, and optimum resource utilization. The actual work develops a mobile network simulation module centered on the 4G Core network comprising its major components; MME, SGW, and PGW (vEPC), as well as the networking amongst these entities, core network related aspects such as its interaction with the Enhanced Radio Access Network (E-UTRAN) and the Packet Data Network Services (Internet). This solution gives to core network engineers and EPC network agents the possibility to design, analyze and test variable types of network scenarios. The solution can be also integrated in engineering schools and college for labs’ practical work or to any e-laboratory initiative to allow students in virtual environment to analyze signaling, modify the network configuration and better understand some theoretical concepts taught during the courses. The results obtained from the present work can be of great help during the final deployment phase of the network for full production for engineers of carriers’ network or improve the engineering understanding of core network in academic domain. In this light, the network can easily be scaled, adjust virtually for the time to commercialize a service reduced for carrier or enterprises’ solutions or for testing in e-laboratories.

Context-Based e2e Autonomous Operation in B5G Networks.

The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One of the most challenging objectives of beyond 5G (B5G) and sixth-generation (6G) networks is the deployment of mechanisms that enable smart end-to-end (e2e) network operation, which is required for the achievement of the stringent service requirements of the envisioned use cases to be supported in the short term. Therefore, smart actions, such as dynamic capacity allocation, flexible functional split, and dynamic slice management need to be performed in tight coordination with the autonomous capacity management of the fixed transport network infrastructure. Otherwise, the benefits of smart slice operation (i.e., cost and energy savings while ensuring per-slice service requirements) might be cancelled due to uncoordinated autonomous fixed network operation. Notably, the transport network in charge of supporting slices from the user equipment (UE) to the core expands across access and metro fixed networks. The required coordination needs to be performed while keeping the privacy of the radio and fixed network domains, which is important in multi-tenant scenarios where both network segments are managed by different operators. In this paper, we propose a novel approach that explores the concept of context-aware network operation, where the slice control anticipates the aggregated and anonymized information of the expected slice operation that is sent to the fixed network orchestrator in an asynchronous way. The context is then used as the input for the artificial intelligence (AI)-based models used by the fixed network control for the predictive capacity management of optical connections in support of RAN slices. This context-aware network operation aims at enabling accurate and reliable autonomous fixed network operation under extremely dynamic traffic originated by smart RAN operation. The exhaustive numerical results show that slice context availability improves the benchmarking fixed network predictive methods (90% reduction in prediction maximum error) remarkably in the foreseen B5G scenarios, for both access and metro segments and in heterogeneous service demand scenarios. Moreover, context-aware network operation enables robust and efficient operation of optical networks in support of dense RAN cells (>32 base stations per cell), while the benchmarking methods fail to guarantee different operational objectives.

An Intelligent Bayesian Inference Based Learning Automaton Approach for Traffic Management in Radio Access Network

An Intelligent Bayesian Inference Based Learning Automaton Approach for Traffic Management in Radio Access Network

A QoS Improving Downlink Scheduling Scheme for Slicing in 5G Radio Access Network (RAN)

A QoS Improving Downlink Scheduling Scheme for Slicing in 5G Radio Access Network (RAN)

Hierarchical Intelligent Radio Access Network Slicing for Differential Service Level Agreement Guaranteeing

Hierarchical Intelligent Radio Access Network Slicing for Differential Service Level Agreement Guaranteeing

The drift handling framework for open radio access networks: An experimental evaluation

The Open Radio Access Network (i.e., Open RAN) aims to transform the inflexible, proprietary RAN into one that is flexible, programmable, and disaggregated to provide intelligent solutions at the network edge for delivering the highly dynamic service demands of Beyond 5G (B5G) networks. Open RAN Working Group-2 (WG2) focuses on the architecture and standards of Artificial Intelligence/Machine Learning (AI/ML) to meet the Service Level Agreement (SLA) requirements for emerging use cases. The inherent dynamic nature of B5G networks and their applications often leads to AI/ML model performance degradation (i.e., drift), resulting in violations of SLAs, over- or under-provisioning of resources, etc.This paper proposes a drift handling framework which includes drift detection, analysis, and its adaptation to minimize the SLA violations and efficient resource utilization. The proposed drift handling framework is evaluated across three diverse use cases: (i) Quality of Service prediction, (ii) Channel Quality Indicator prediction, and (iii) User traffic prediction. The experimental evaluations are performed by using the Open RAN Software Community, and OpenAirInterface FlexRIC platforms with a real-time dataset. Results demonstrate that the proposed drift handling framework outperforms all other considered state-of-the art frameworks for all the scenarios in terms of the drift detection time, accuracy, precision, F1-score, etc.

Multi-Objective Multi-Dimensional Resource Allocation for Categorized QoS Provisioning in Beyond 5G and 6G Radio Access Networks

Multi-Objective Multi-Dimensional Resource Allocation for Categorized QoS Provisioning in Beyond 5G and 6G Radio Access Networks

Hierarchical Slicing: A New Paradigm of Radio Resource Management for Mobile Networks

Radio access network slicing is considered to be highly challenging due to the complexity of mobile environments and the diversity of mobile services. Existing works in this area mainly decompose the radio resource management problem into slice-level resource management and user-level resource scheduling. In this paper, we propose a hierarchical three-tier slicing architecture, where an additional tier is introduced to merge the gap between the conventional two tiers in time and spatial scales. Specifically, the medium-scale tier provides intercell and inter-slice coordination to address short-term dynamics caused by local user mobility and traffic variations, which highly simplifies the upper and lower tiers by allowing them to focus on long-term traffic distribution and instant user demands, respectively. Thus, it allows each independent iter to apply more flexible and more efficient radio resource management methods with appropriate time and spatial scales. A proof of concept is provided to show that the proposed three-tier architecture can achieve a better tradeoff between slice isolation and slice capacity as compared to the state-of-the-art two-tier solution.

Energy Sustainability in Dense Radio Access Networks via High Altitude Platform Stations

Energy Sustainability in Dense Radio Access Networks via High Altitude Platform Stations

Multistage Decimator Design for the Low-PHY PRACH Receiver

This paper describes the design of a multistage decimator for the Physical Random Access Channel (PRACH) receiver based on the hybrid time/frequency domain architecture. Flexible structure of the multistage decimator allows to support all combinations of PRACH subcarrier spacings and carrier bandwidths defined in the 3rd Generation Partnership Project (3GPP) Release 15 for Frequency Range (FR) 1. Impulse responses of low pass filters for each decimation stage do not depend on a selected combination allowing to facilitate hardware implementation. The multistage decimator jointly uses the Cascaded Integrator-Comb (CIC) and Nyquist filters to minimize number of required multipliers. Acceptable degradation level due to decimation in the receiver performance is estimated based on requirements defined by the Radio Access Network (RAN) working group 4. Assuming no specific detection algorithm, it is shown that the proposed design introduces negligible degradation, less than 0.1 dB in the operating Signal-to-Noise Ratio (SNR) region.

Queuing Network Models of Multiservice RANs

In this paper, we present a new queuing network model for the analysis of a portion of a radio access network (RAN) comprising macro cell base stations (BSs) and small cell BSs offering ”streaming” and ”elastic” services. Streaming services require a certain data rate for a random time. The required data rates depend on the type of service, e.g., audio and video. Elastic services require the transfer of random data volumes, and their data rate adjusts dynamically based on the capacity not utilized by the streaming services. To derive performance measures for the proposed model we develop a computationally efficient framework that exploits a new product form result for streaming services, relying on a well-known blocking policy, and an approximate product form for elastic services. Insensitivity to the distribution of service requirements holds in the case of negligible end user mobility. We show the high accuracy of our model in predicting the performance of practical system configurations by conducting a thorough comparison between the model’s results and those obtained from a detailed discrete-event simulator. Through this analysis, we uncover significant counter-intuitive behaviors that arise from the competition between streaming services with diverse demands, and that are effectively captured and predicted by our modeling approach. Our computationally efficient queuing model is a useful new tool to support design and planning of multiservice RANs whose complex structures result from the coexistence of BSs of different generations in dense areas.

Relay-empowered beyond 5G radio access networks with edge computing capabilities

Relevant services envisaged for beyond 5G (B5G) systems, such as extended reality and holographic communications, have extremely demanding user experience requirements with significant computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers near the user, the communication will take advantage of the technologies developed for 5G New Radio jointly with an never-before-seen degree of network densification. This paper proposes the use of relays with edge computing capabilities. The approach's potential for B5G are identified, and a system model is defined to characterize both computational and communications viewpoints. Based on this, results are provided to highlight the gains and limitations of the proposed approach from a system-level perspective. Finally, the main challenges for enabling relays with computing capabilities in B5G deployments are discussed.

Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO

Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios.

Delay-sensitive resource allocation for IoT systems in 5G O-RAN networks

The rapid advancement in sensors and communications has led to the expansion of the Internet of Things (IoT) services, where many devices need access to the transport network using fixed or wireless access technologies and mobile Radio Access Networks (RAN). However, supporting IoT in RAN is challenging as IoT services may produce many short and variable sessions, impacting the performance of mobile users sharing the same RAN. To address this issue, network slicing is a promising solution to support heterogeneous service segments sharing the same RAN, which is a crucial requirement of the upcoming fifth-generation (5G) mobile network. This paper proposes a two-level network slicing mechanism for enhanced mobile broadband (eMBB) and Ultra-Reliable and Low Latency communications (URLLC) in order to provide end-to-end slicing at the core and edge of the network with the aim of reducing latency for IoT services and mobile users sharing the same core and RAN using the O-RAN architecture. The problem is modeled at both levels as a Markov decision process (MDP) and solved using hierarchical reinforcement learning. At a high level, an SDN controller using an agent that has been trained by a Double Deep Q-network (DDQN) allocates radio resources to gNodeBs (next-generation NodeB, a 5G base station) based on the requirements of eMBB and URLLC services. At a low level, each gNodeB using an agent that has been trained by a DDQN allocates its pre-allocated resources to its end-users. The proposed approach has been demonstrated and validated through a real testbed. Notably, it surpasses the prevalent approaches in terms of end-to-end latency.

A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas

With the rapid rollout and growing adoption of 3GPP 5thGeneration (5G) cellular services, including in critical infrastructure sectors, it is important to review security mechanisms, risks, and potential vulnerabilities within this vital technology. Numerous security capabilities need to work together to ensure and maintain a sufficiently secure 5G environment that places user privacy and security at the forefront. Confidentiality, integrity, and availability are all pillars of a privacy and security framework that define major aspects of 5G operations. They are incorporated and considered in the design of the 5G standard by the 3rd Generation Partnership Project (3GPP) with the goal of providing a highly reliable network operation for all. Through a comprehensive review, we aim to analyze the ever-evolving landscape of 5G, including any potential attack vectors and proposed measures to mitigate or prevent these threats. This paper presents a comprehensive survey of the state-of-the-art research that has been conducted in recent years regarding 5G systems, focusing on the main components in a systematic approach: the Core Network (CN), Radio Access Network (RAN), and User Equipment (UE). Additionally, we investigate the utilization of 5G in time-dependent, ultra-confidential, and private communications built around a Zero Trust approach. In today’s world, where everything is more connected than ever, Zero Trust policies and architectures can be highly valuable in operations containing sensitive data. Realizing a Zero Trust Architecture entails continuous verification of all devices, users, and requests, regardless of their location within the network, and grants permission only to authorized entities. Finally, developments and proposed methods of new 5G and future 6G security approaches, such as Blockchain technology, post-quantum cryptography (PQC), and Artificial Intelligence (AI) schemes, are also discussed to understand better the full landscape of current and future research within this telecommunications domain.

Agile FPGA Computing at the 5G Edge: Joint Management of Accelerated and Software Functions for Open Radio Access Technologies

This paper presents ReproRun, a flexible and extensible run-time framework for the reconfiguration of functions in field programmable gate array (FPGA) devices used in popular software-defined radio (SDR) platforms. The FPGA devices embed a hardwired or soft processing system (PS) which communicates with the programmable logic (PL) using a standard embedded bus interface. In order to apply a seamless run-time partial reconfiguration, we made use of all the related building blocks, design guidelines, and tools offered by AMD-Xilinx. In ReproRun, each partial bitstream targeting a reconfigurable region (RR) of the PL area comes with its respective firmware (i.e., software functions) that runs on the PS side. Our work guarantees run-time updates of the firmware without interrupting the functionality of other software processes running in the PS or PL, by employing a specialized controller, denoted as Run-timE firmWare reconfIguration contRollEr (REWIRE). The latter leverages the open asymmetric multiprocessing framework (OpenAMP). The partial bitstreams and respective firmware are fetched from a remote location using the trivial file transfer protocol (TFTP). ReproRun can be applied in different FPGA accelerators residing in disaggregated open radio access network (RAN) equipment, adaptive radio access technologies, and Edge servers hosting virtualized functions.

Converging Towards Open Radio Access Networks – A Comprehensive Review

Radio Access Networks (RAN) have been an integral part of the cellular mobile communication systems since the deployment of Global System for Mobile Communication (GSM) networks and later for the legacy Universal Mobile Telecommunication Systems (UMTS) and Long Term Evolution (LTE) networks. However, due to increasing demands of the users, throughput, ultra-lower latency, virtualization of the network and to cater the seamless connectivity of millions of wireless devices with the cellular networks, the advent of RAN needs to be brought under consideration. In this paper the traditional RANs are discussed with the necessity for their transition into the Open RAN (ORAN), considering all its essential parameters. The constraints of the legacy RAN architectures are explored with an overview of the RAN intelligent controllers, ORAN and its types. This paper additionally examines the function of artificial intelligence in Common Public Radio Interface (CPRI), enhanched CPRI, and xApps in terms of use cases along with the challenges associated with their deployment. The paper also present challenges and future of ORAN.

A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches

The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN’s deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.

Intelligent RAN Automation for 5G and Beyond

5G is becoming more agile and inevitably more complicated for supporting various deployment scenarios and diversified applications. Artificial Intelligence (AI) is becoming one of the most essential driving forces for 5G evolution to handle the complexity and further improve the network spectrum efficiency, energy efficiency and user experience. In this article, we share the latest standardization progress of the industry on AI in 5G. Evolving directions for the intelligent radio access networks (RAN) are analyzed and further enhancements on both AI for RAN and RAN for AI are proposed based on a hierarchical and distributed intelligent RAN architecture. Case studies are presented along with field trial results. An innovative AI-empowered RAN and service-aware cross layer optimization framework is illustrated for the quality of experience (QoE) optimization of cloud virtual reality (VR) with converged communication and computing services provided by RAN. The extension of the solution to support the AI/ML services is also discussed to shed some light on how to evolve network for AI towards the 6G vision.

End to End AI Architecture for Next Generation Network

Targeting 6G network architecture with native intelligence, a holistic view on the end to end (E2E) Artificial Intelligence (AI) flow across Operation Administration and Maintenance (OAM), Core Network (CN), Edge, Radio Access Network (RAN), and User Equipment (UE) domains is presented in this article. The AI Task Management (AITM) is introduced as the enabler in such network, and a User Equipment Data Analytics (UEDA) is proposed to take advantage of the increasing computing capability of UEs while taking their power consumption limitation into consideration. Conventional control plane and user plane connections assume fixed one-to-one end points and a single chosen transmission mode. Depending on the goal of the AI task and what network entities need to participate, the endpoints of the AI task connection is uncertain, with a group of participant UEs on one side and base station, core network or application server on the other. The transmission mode of such connection may need to be dynamically reconfigured as well. This article proposes an approach of AI task-oriented connections, where logical connections among UE, RAN, CN, and applications are established via AITM for AI model distribution, data collection, as well as parameter synchronization across different domains. A new wireless bearer type, named AI Radio Bearer (AIRB), for AI task-oriented connections is proposed with due design considerations and its associated performance.